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ResearchFormerly WizWand

Policy Decision Making on Synthetic d_phi=2 out-sample

5.64Policy Error Rate (ER_out)InvTARNet
5.158.457511.76515.0725Nov 19, 2023
Updated 7mo ago

Evaluation results

MethodLinks
InvTARNetRepresentation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.115.64-0.02
CFR-ISW (WM; α = 1.0)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.117.27-1.86
BWCFR (WM; α = 1.0)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.117.44-4.57
RCFR (WM; α = 1.0)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.118-4.27
k-NNNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.118.18—
TARNetRepresentation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.119.82-3.73
CFR (WM; α = 1.0)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1110.88-7.97
CFR (MMD; α = 0.1)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1111.92-5.54
CFR (WM; α = 2.0)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1113.19-6.28
C-ForestNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1116.1—
BNN (MMD; α = 0.1)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1116.15-4.19
BARTNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1117.37—
CFR (MMD; α = 0.5)Representation dimension (d_phi)=2, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.1117.89-7.27